The \(\sf {ReLu}\)  function greatly accelerates the convergence of optimization algorithm during training process compared to the sigmoid or tanh activation functions [1]}.
The network is finalized with a \(\sf relu\)  layer between two \(\sf fc\)  layers, denoted by \(\sf fc1\)  and \(\sf fc2\)  in Fig. REF , wherein the number of neurons in \(\sf fc2\)  is identical to the number of estimation variables at the output. Here, the BLER and throughput variables need to be estimated and they are arranged into a vector \(\textbf {y}\triangleq [\varepsilon _{e2e} \,\, \tau _{e2e}]\) .
With regard to the layer configurations, we specify 64 kernels in each \(\sf conv\)  layer and 64 neurons in each \(\sf fc\)  layer. Based on the universal approximation theory [2]}, the heuristic-based scaling approach is applied in this design, where the number of kernels and neurons are heuristically chosen by experimental trials until the minimal training error is achieved [3]}.
It is worth noting that the zero padding is automatically added when processing convolution with different kernel sizes to keep the spatial size of output feature maps unchangeable.
